Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 344
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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training_log.csv
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| 342 |
341.0,0.34450850300467206,0.7211762952660077,0.14772874116897583,14.5084,177.346,5.583,0.25612125923047024,224378
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| 343 |
342.0,0.3430837119854913,0.720640923566879,0.14765557646751404,14.6403,175.748,5.533,0.2565099106101827,225036
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| 344 |
343.0,0.3428549185783561,0.7210539398458862,0.14768673479557037,14.4458,178.114,5.607,0.25806451612903225,225694
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| 342 |
341.0,0.34450850300467206,0.7211762952660077,0.14772874116897583,14.5084,177.346,5.583,0.25612125923047024,224378
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| 343 |
342.0,0.3430837119854913,0.720640923566879,0.14765557646751404,14.6403,175.748,5.533,0.2565099106101827,225036
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| 344 |
343.0,0.3428549185783561,0.7210539398458862,0.14768673479557037,14.4458,178.114,5.607,0.25806451612903225,225694
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| 345 |
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344.0,0.343411552351322,0.7208237986270023,0.14769762754440308,14.3332,179.514,5.651,0.25767586474931986,226352
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